First commit

vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py

@@ -0,0 +1,412 @@
+from typing import Any, Dict, List, Optional, cast
+
+import torch
+from pydantic import BaseModel
+
+from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.linear import (LinearBase, LinearMethodBase,
+                                               UnquantizedLinearMethod)
+from vllm.model_executor.layers.quantization.base_config import (  # noqa: E501
+    QuantizationConfig, QuantizeMethodBase)
+from vllm.model_executor.layers.quantization.compressed_tensors.compressed_tensors_moe import (  # noqa: E501
+    CompressedTensorsMoEMethod)
+from vllm.model_executor.layers.quantization.compressed_tensors.schemes import (
+    W4A16SPARSE24_SUPPORTED_BITS, WNA16_SUPPORTED_BITS,
+    CompressedTensorsScheme, CompressedTensorsW4A16Sparse24,
+    CompressedTensorsW8A8Fp8, CompressedTensorsW8A8Int8,
+    CompressedTensorsW8A16Fp8, CompressedTensorsWNA16)
+from vllm.model_executor.layers.quantization.compressed_tensors.utils import (
+    CompressionFormat, QuantizationArgs, QuantizationStrategy,
+    QuantizationType, find_matched_target, is_activation_quantization_format,
+    should_ignore_layer)
+from vllm.model_executor.layers.quantization.kv_cache import BaseKVCacheMethod
+from vllm.platforms import current_platform
+
+__all__ = ["CompressedTensorsLinearMethod"]
+
+
+class CompressedTensorsConfig(QuantizationConfig):
+
+    def __init__(self,
+                 target_scheme_map: Dict[str, Any],
+                 ignore: List[str],
+                 quant_format: str,
+                 kv_cache_scheme: Optional[Dict[str, Any]] = None):
+
+        self.ignore = ignore
+        self.quant_format = quant_format
+        # Map from [target -> scheme]
+        self.target_scheme_map = target_scheme_map
+        self.kv_cache_scheme = kv_cache_scheme
+
+    def get_linear_method(self) -> "CompressedTensorsLinearMethod":
+        return CompressedTensorsLinearMethod(self)
+
+    def get_scaled_act_names(self) -> List[str]:
+        return []
+
+    def get_supported_act_dtypes(cls) -> List[torch.dtype]:
+        return [torch.float16, torch.bfloat16]
+
+    @classmethod
+    def get_min_capability(cls) -> int:
+        return 70
+
+    def get_name(self) -> str:
+        return "compressed_tensors"
+
+    def get_quant_method(
+        self,
+        layer: torch.nn.Module,
+        prefix: str,
+    ) -> Optional["QuantizeMethodBase"]:
+        from vllm.attention.layer import Attention  # Avoid circular import
+
+        # Check if the layer is skipped for quantization.
+        # TODO (@robertgshaw2): support module names
+        if should_ignore_layer(prefix, ignore=self.ignore):
+            return UnquantizedLinearMethod()
+        if isinstance(layer, LinearBase):
+            scheme = self.get_scheme(layer=layer, layer_name=prefix)
+            layer.scheme = scheme
+            return CompressedTensorsLinearMethod(self)
+        if isinstance(layer, Attention):
+            return CompressedTensorsKVCacheMethod(self)
+        if isinstance(layer, FusedMoE):
+            return CompressedTensorsMoEMethod.get_moe_method(self)
+        return None
+
+    @classmethod
+    def from_config(cls, config: Dict[str, Any]) -> "CompressedTensorsConfig":
+        target_scheme_map: Dict[str, Any] = dict()
+        ignore = cast(List[str], config.get("ignore"))
+        quant_format = cast(str, config.get("format"))
+
+        # The quant_config has multiple config_groups, each containing
+        # an input_activations key with details about how the activations are
+        # quantized, a weights key indicating how the weights are quantized,
+        # and a list of targets under the `targets` key, dictating which
+        # layers are impacted by the quantization details. The quantization
+        # details follow the structure defined by the QuantizationArgs
+        # pydantic model, which is used to verify the structure of the
+        # quant_config and also store the details for later use.
+        for _, quant_config in config["config_groups"].items():
+            targets = quant_config.get("targets")
+            for target in targets:
+                target_scheme_map[target] = {}
+                target_scheme_map[target][
+                    "weights"] = QuantizationArgs.parse_obj(
+                        quant_config.get("weights"))
+                try:
+                    target_scheme_map[target][
+                        "input_activations"] = QuantizationArgs.parse_obj(
+                            quant_config.get("input_activations"))
+                except Exception:
+                    target_scheme_map[target]["input_activations"] = None
+
+        return cls(target_scheme_map=target_scheme_map,
+                   ignore=ignore,
+                   quant_format=quant_format,
+                   kv_cache_scheme=config.get("kv_cache_scheme"))
+
+    @classmethod
+    def get_config_filenames(cls) -> List[str]:
+        return []
+
+    def _check_scheme_supported(self,
+                                min_capability: int,
+                                error: bool = True) -> bool:
+        capability_tuple = current_platform.get_device_capability()
+
+        if capability_tuple is not None:
+            capability = capability_tuple.to_int()
+            supported = capability >= min_capability
+            if error and not supported:
+                raise RuntimeError(
+                    "Quantization scheme is not supported for ",
+                    f"the current GPU. Min capability: {min_capability}. ",
+                    f"Current capability: {capability}.")
+            return supported
+        else:
+            return False
+
+    def _is_static_tensor_w8a8(self, weight_quant: BaseModel,
+                               input_quant: BaseModel) -> bool:
+        is_8_bits = weight_quant.num_bits == input_quant.num_bits == 8
+        weight_strategy = (
+            weight_quant.strategy == QuantizationStrategy.TENSOR.value
+            or weight_quant.strategy == QuantizationStrategy.CHANNEL.value)
+        is_tensor = (weight_strategy and input_quant.strategy
+                     == QuantizationStrategy.TENSOR.value)
+        is_static = not weight_quant.dynamic and not input_quant.dynamic
+
+        # Both symmetric and asymmetric input quantization supported.
+        # Only symmetric weight quantization supported.
+        return is_8_bits and is_tensor and weight_quant.symmetric and is_static
+
+    def _is_dynamic_token_w8a8(self, weight_quant: BaseModel,
+                               input_quant: BaseModel) -> bool:
+        is_8_bits = weight_quant.num_bits == input_quant.num_bits == 8
+        weight_strategy = (
+            weight_quant.strategy == QuantizationStrategy.TENSOR.value
+            or weight_quant.strategy == QuantizationStrategy.CHANNEL.value)
+        is_token = (weight_strategy and input_quant.strategy
+                    == QuantizationStrategy.TOKEN.value)
+        is_dynamic = not weight_quant.dynamic and input_quant.dynamic
+
+        # Both symmetric and asymmetric input quantization supported.
+        # Only symmetric weight quantization supported.
+        return is_8_bits and is_token and weight_quant.symmetric and is_dynamic
+
+    def _is_fp8_w8a8(self, weight_quant: BaseModel,
+                     input_quant: BaseModel) -> bool:
+        # Confirm weights and activations quantized.
+        if weight_quant is None or input_quant is None:
+            return False
+
+        # Confirm weight scheme is supported.
+        is_floating_point = (weight_quant.type == QuantizationType.FLOAT
+                             and input_quant.type == QuantizationType.FLOAT)
+        is_symmetric_weight = weight_quant.symmetric
+        is_static_weight = not weight_quant.dynamic
+        is_per_tensor_or_channel_weight = (weight_quant.strategy in [
+            QuantizationStrategy.TENSOR, QuantizationStrategy.CHANNEL
+        ])
+        if not (is_floating_point and is_symmetric_weight and is_static_weight
+                and is_per_tensor_or_channel_weight):
+            return False
+
+        # Dynamic quantization is always supported if weights supported.
+        if input_quant.dynamic:
+            return True
+
+        # Confirm activation scheme is supported.
+        is_symmetric_activation = input_quant.symmetric
+        is_per_tensor_activation = (
+            input_quant.strategy == QuantizationStrategy.TENSOR)
+        return is_symmetric_activation and is_per_tensor_activation
+
+    def _is_fp8_w8a16(self, weight_quant: BaseModel,
+                      input_quant: BaseModel) -> bool:
+        # Confirm weights quantized.
+        if weight_quant is None:
+            return False
+
+        # Confirm we have floating points.
+        if weight_quant.type != QuantizationType.FLOAT:
+            return False
+
+        # Confirm weight scheme is supported.
+        is_symmetric_weight = weight_quant.symmetric
+        is_static_weight = not weight_quant.dynamic
+        is_per_tensor_or_channel_weight = (weight_quant.strategy in [
+            QuantizationStrategy.TENSOR, QuantizationStrategy.CHANNEL
+        ])
+        if not (is_symmetric_weight and is_static_weight  # noqa: SIM103
+                and is_per_tensor_or_channel_weight):
+            return False
+
+        # All conditions satisfied.
+        return True
+
+    def _is_wNa16_group_channel(self, weight_quant: BaseModel,
+                                input_quant: BaseModel) -> bool:
+        input_quant_none = input_quant is None
+        is_symmetric = weight_quant.symmetric
+        is_channel_group = (
+            weight_quant.strategy == QuantizationStrategy.CHANNEL.value
+            or weight_quant.strategy == QuantizationStrategy.GROUP.value)
+        is_static = not weight_quant.dynamic
+
+        return (is_channel_group and input_quant_none and is_symmetric
+                and is_static)
+
+    def _get_scheme_from_parts(
+            self, weight_quant: BaseModel,
+            input_quant: BaseModel) -> "CompressedTensorsScheme":
+
+        # Detect If Mixed Precision
+        if self._is_wNa16_group_channel(weight_quant, input_quant):
+            if (self.quant_format == CompressionFormat.marlin_24.value
+                    and weight_quant.num_bits in W4A16SPARSE24_SUPPORTED_BITS):
+                return CompressedTensorsW4A16Sparse24(
+                    strategy=weight_quant.strategy,
+                    num_bits=weight_quant.num_bits,
+                    group_size=weight_quant.group_size)
+            if (self.quant_format == CompressionFormat.pack_quantized.value
+                    and weight_quant.num_bits in WNA16_SUPPORTED_BITS):
+                return CompressedTensorsWNA16(
+                    num_bits=weight_quant.num_bits,
+                    strategy=weight_quant.strategy,
+                    group_size=weight_quant.group_size,
+                    actorder=weight_quant.actorder)
+
+        # Detect If Activation Quantization.
+        # TODO @dsikka: clean-up conditions
+        if is_activation_quantization_format(self.quant_format):
+            if self._is_fp8_w8a8(weight_quant, input_quant):
+                is_fp8_w8a8_supported = self._check_scheme_supported(
+                    CompressedTensorsW8A8Fp8.get_min_capability(), error=False)
+                if is_fp8_w8a8_supported:
+                    return CompressedTensorsW8A8Fp8(
+                        strategy=weight_quant.strategy,
+                        is_static_input_scheme=(input_quant
+                                                and not input_quant.dynamic))
+                else:
+                    return CompressedTensorsW8A16Fp8(
+                        strategy=weight_quant.strategy,
+                        is_static_input_scheme=(input_quant
+                                                and not input_quant.dynamic))
+
+            if self._is_fp8_w8a16(weight_quant, input_quant):
+                return CompressedTensorsW8A16Fp8(
+                    strategy=weight_quant.strategy,
+                    is_static_input_scheme=(input_quant
+                                            and not input_quant.dynamic))
+
+            if self._is_static_tensor_w8a8(weight_quant, input_quant):
+                return CompressedTensorsW8A8Int8(
+                    strategy=weight_quant.strategy,
+                    is_static_input_scheme=True,
+                    input_symmetric=input_quant.symmetric)
+
+            if self._is_dynamic_token_w8a8(weight_quant, input_quant):
+                return CompressedTensorsW8A8Int8(
+                    strategy=weight_quant.strategy,
+                    is_static_input_scheme=False,
+                    input_symmetric=input_quant.symmetric)
+
+        raise NotImplementedError(
+            "No compressed-tensors compatible scheme was found.")
+
+    def get_scheme(
+            self,
+            layer: torch.nn.Module,
+            layer_name: Optional[str] = None) -> "CompressedTensorsScheme":
+        """
+        compressed-tensors supports non uniform in the following way:
+
+        ignore: List of layer_names or nn.Module names to be ignored.
+        targets of config_groups: There can be N config_groups which each
+            have a quantization scheme. Each config_group has a list of targets
+            which can be a full layer_name, a regex for a layer_name, or
+            an nn.Module name.
+
+        We first check whether a layer is in the ignore group and use
+        CompressedTensorsUnquantized (i.e. fp16/bf16) scheme for the layer
+
+        We then detect whether a layer_name is found in any target and
+        use the quantization scheme corresponding to the matched target
+        to select the CompressedTensorsScheme used for infernece.
+        """
+
+        # Find the "target" in the compressed-tensors config
+        # that our layer conforms to.
+        # TODO (@robertgshaw): add compressed-tensors as dep
+        # so we do not have to re-write these functions
+        # need to make accelerate optional in ct to do this
+        matched_target = find_matched_target(
+            layer_name=layer_name,
+            module=layer,
+            targets=self.target_scheme_map.keys())
+
+        # Find the quant_scheme
+        scheme_dict = self.target_scheme_map[matched_target]
+        scheme = self._get_scheme_from_parts(
+            weight_quant=scheme_dict["weights"],
+            input_quant=scheme_dict["input_activations"])
+
+        # Raise error if device does not support the scheme
+        # (e.g. fp8 needs ada lovelace)
+        self._check_scheme_supported(scheme.get_min_capability())
+
+        return scheme
+
+
+class CompressedTensorsLinearMethod(LinearMethodBase):
+
+    def __init__(self, quantization_config: CompressedTensorsConfig):
+        self.quantization_config = quantization_config
+
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+        layer.scheme.process_weights_after_loading(layer)
+
+    def create_weights(self, layer: torch.nn.Module,
+                       input_size_per_partition: int,
+                       output_partition_sizes: List[int], input_size: int,
+                       output_size: int, params_dtype: torch.dtype,
+                       **extra_weight_attrs):
+        """
+        Use the CompressedTensorsScheme associated with each layer to create
+        the necessary parameters for the layer. See LinearMethodBase for param
+        details
+        """
+        weight_loader = extra_weight_attrs.get("weight_loader")
+        layer.scheme.create_weights(
+            layer=layer,
+            input_size=input_size,
+            input_size_per_partition=input_size_per_partition,
+            output_partition_sizes=output_partition_sizes,
+            output_size=output_size,
+            params_dtype=params_dtype,
+            weight_loader=weight_loader)
+
+    def apply(self,
+              layer: torch.nn.Module,
+              x: torch.Tensor,
+              bias: Optional[torch.Tensor] = None):
+        """
+        Use the output of create_weights and the CompressedTensorsScheme
+        associated with the layer to apply the forward pass with the
+        layer input.  See LinearMethodBase for param details
+
+        """
+
+        scheme = layer.scheme
+        if scheme is None:
+            raise ValueError("A scheme must be defined for each layer")
+        return scheme.apply_weights(layer, x, bias=bias)
+
+
+class CompressedTensorsKVCacheMethod(BaseKVCacheMethod):
+    """
+    Supports loading kv-cache scaling factors from compressed-tensors
+    checkpoints.
+    """
+
+    def __init__(self, quant_config: CompressedTensorsConfig):
+        self.validate_kv_cache_scheme(quant_config.kv_cache_scheme)
+        super().__init__(quant_config)
+
+    @staticmethod
+    def validate_kv_cache_scheme(kv_cache_scheme: Optional[Dict[str, Any]]):
+        """
+        Validator for the kv cache scheme. Useful for controlling the
+        kv cache quantization schemes, that are being supported in vLLM
+        :param kv_cache_scheme: the compressed-tensors kv cache scheme
+        """
+        if kv_cache_scheme is None:
+            return
+
+        type_ = kv_cache_scheme.get("type")
+        num_bits = kv_cache_scheme.get("num_bits")
+
+        if type_ != "float" and num_bits != 8:
+            raise NotImplementedError(
+                "Currently supported kv cache quantization is "
+                "num_bits=8, type=float, however "
+                f"received num_bits={num_bits}, type={type_}")
+
+        strategy = kv_cache_scheme.get("strategy")
+        if strategy != "tensor":
+            raise NotImplementedError(
+                "Only support per-tensor scaling factor "
+                "for compressed-tensors KV cache. "
+                f"Expected strategy: tensor, found strategy: {strategy}")
+
+        is_symmetric = kv_cache_scheme.get("symmetric")
+        if not is_symmetric:
+            raise NotImplementedError(
+                "Only support symmetric scaling factor "
+                "for compressed-tensors KV cache. "
+                f"However found symmetric: {is_symmetric}")

vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe.py

@@ -0,0 +1,511 @@
+import enum
+from enum import Enum
+from typing import Callable, List, Optional
+
+import torch
+
+from vllm import _custom_ops as ops
+from vllm.model_executor.layers.fused_moe import (FusedMoE, FusedMoEMethodBase,
+                                                  FusedMoeWeightScaleSupported)
+from vllm.model_executor.layers.quantization.compressed_tensors.schemes import (
+    WNA16_SUPPORTED_BITS)
+from vllm.model_executor.layers.quantization.compressed_tensors.utils import (
+    CompressionFormat, QuantizationStrategy)
+from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
+    all_close_1d, normalize_e4m3fn_to_e4m3fnuz, per_tensor_dequantize)
+from vllm.model_executor.utils import set_weight_attrs
+from vllm.utils import is_hip, print_warning_once
+
+
+class GPTQMarlinState(Enum):
+    REPACK = enum.auto()
+    READY = enum.auto()
+
+
+__all__ = [
+    "CompressedTensorsMoEMethod", "CompressedTensorsW8A8Fp8MoEMethod",
+    "CompressedTensorsWNA16MoEMethod"
+]
+
+
+class CompressedTensorsMoEMethod(FusedMoEMethodBase):
+
+    @staticmethod
+    def get_moe_method(
+        quant_config: "CompressedTensorsConfig"  # type: ignore # noqa E501
+    ) -> "CompressedTensorsMoEMethod":
+        # TODO: @dsikka: refactor this to use schemes as other kernels
+        # are supported + check if the layer is being ignored.
+        weight_quant = quant_config.target_scheme_map["Linear"].get("weights")
+        input_quant = quant_config.target_scheme_map["Linear"].get(
+            "input_activations")
+
+        if quant_config._is_wNa16_group_channel(weight_quant, input_quant):
+            return CompressedTensorsWNA16MoEMethod(quant_config)
+        elif quant_config._is_fp8_w8a8(weight_quant, input_quant):
+            return CompressedTensorsW8A8Fp8MoEMethod(quant_config)
+        else:
+            raise RuntimeError(
+                f"Unsupported FusedMoe scheme: {weight_quant}, {input_quant}")
+
+
+class CompressedTensorsW8A8Fp8MoEMethod(CompressedTensorsMoEMethod):
+
+    def __init__(
+            self,
+            quant_config: "CompressedTensorsConfig"  # type: ignore # noqa E501
+    ):
+        self.quant_config = quant_config
+        self.weight_quant = self.quant_config.target_scheme_map["Linear"].get(
+            "weights")
+        self.input_quant = self.quant_config.target_scheme_map["Linear"].get(
+            "input_activations")
+
+        if not (self.weight_quant.strategy == QuantizationStrategy.TENSOR
+                and self.input_quant.strategy == QuantizationStrategy.TENSOR):
+            raise ValueError(
+                "For FP8 Fused MoE layers, only per-tensor scales"
+                "for weights and activations are supported. Found "
+                f"{self.weight_quant}, {self.input_quant}")
+
+        self.static_input_scales = not self.input_quant.dynamic
+
+    def create_weights(self, layer: torch.nn.Module, num_experts: int,
+                       hidden_size: int, intermediate_size: int,
+                       params_dtype: torch.dtype, **extra_weight_attrs):
+
+        params_dtype = torch.float8_e4m3fn
+
+        # WEIGHTS
+        w13_weight = torch.nn.Parameter(torch.empty(num_experts,
+                                                    2 * intermediate_size,
+                                                    hidden_size,
+                                                    dtype=params_dtype),
+                                        requires_grad=False)
+        layer.register_parameter("w13_weight", w13_weight)
+        set_weight_attrs(w13_weight, extra_weight_attrs)
+
+        w2_weight = torch.nn.Parameter(torch.empty(num_experts,
+                                                   hidden_size,
+                                                   intermediate_size,
+                                                   dtype=params_dtype),
+                                       requires_grad=False)
+        layer.register_parameter("w2_weight", w2_weight)
+        set_weight_attrs(w2_weight, extra_weight_attrs)
+
+        # WEIGHT_SCALES
+        # Allocate 2 scales for w1 and w3 respectively.
+        # They will be combined to a single scale after weight loading.
+        w13_weight_scale = torch.nn.Parameter(torch.ones(num_experts,
+                                                         2,
+                                                         dtype=torch.float32),
+                                              requires_grad=False)
+        layer.register_parameter("w13_weight_scale", w13_weight_scale)
+
+        w2_weight_scale = torch.nn.Parameter(torch.ones(num_experts,
+                                                        dtype=torch.float32),
+                                             requires_grad=False)
+        layer.register_parameter("w2_weight_scale", w2_weight_scale)
+        # Add the quantization method used (per tensor/grouped/channel)
+        # to ensure the weight scales are loaded in properly
+        extra_weight_attrs.update(
+            {"quant_method": FusedMoeWeightScaleSupported.TENSOR.value})
+        set_weight_attrs(w13_weight_scale, extra_weight_attrs)
+        set_weight_attrs(w2_weight_scale, extra_weight_attrs)
+
+        # INPUT_SCALES
+        if self.static_input_scales:
+            w13_input_scale = torch.nn.Parameter(torch.ones(
+                num_experts, dtype=torch.float32),
+                                                 requires_grad=False)
+            layer.register_parameter("w13_input_scale", w13_input_scale)
+            set_weight_attrs(w13_input_scale, extra_weight_attrs)
+
+            w2_input_scale = torch.nn.Parameter(torch.ones(
+                num_experts, dtype=torch.float32),
+                                                requires_grad=False)
+            layer.register_parameter("w2_input_scale", w2_input_scale)
+            set_weight_attrs(w2_input_scale, extra_weight_attrs)
+        else:
+            layer.w13_input_scale = None
+            layer.w2_input_scale = None
+
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+        # Fp8 moe kernels require a single activation scale.
+        # We take the max of all the scales in case they differ.
+        if self.static_input_scales:
+            if (layer.w13_input_scale is None or layer.w2_input_scale is None):
+                raise ValueError(
+                    "QuantConfig has static quantization, but found "
+                    "activation scales are None.")
+            if (not all_close_1d(layer.w13_input_scale)
+                    or not all_close_1d(layer.w2_input_scale)):
+                print_warning_once(
+                    "Found input_scales that are not equal for "
+                    "fp8 MoE layer. Using the maximum across experts "
+                    "for each layer. ")
+            layer.w13_input_scale = torch.nn.Parameter(
+                layer.w13_input_scale.max(), requires_grad=False)
+            layer.w2_input_scale = torch.nn.Parameter(
+                layer.w2_input_scale.max(), requires_grad=False)
+
+        # If rocm, normalize the weights and scales to e4m3fnuz
+        if is_hip():
+            # Normalize the weights and scales
+            w13_weight, w13_weight_scale, w13_input_scale = \
+                normalize_e4m3fn_to_e4m3fnuz(
+                    layer.w13_weight, layer.w13_weight_scale,
+                    layer.w13_input_scale)
+            w2_weight, w2_weight_scale, w2_input_scale = \
+                normalize_e4m3fn_to_e4m3fnuz(
+                    layer.w2_weight, layer.w2_weight_scale,
+                    layer.w2_input_scale)
+            # Reset the parameter
+            layer.w13_weight = torch.nn.Parameter(w13_weight,
+                                                  requires_grad=False)
+            layer.w13_weight_scale = torch.nn.Parameter(w13_weight_scale,
+                                                        requires_grad=False)
+            if w13_input_scale is not None:
+                layer.w13_input_scale = torch.nn.Parameter(w13_input_scale,
+                                                           requires_grad=False)
+            layer.w2_weight = torch.nn.Parameter(w2_weight,
+                                                 requires_grad=False)
+            layer.w2_weight_scale = torch.nn.Parameter(w2_weight_scale,
+                                                       requires_grad=False)
+            if w2_input_scale is not None:
+                layer.w2_input_scale = torch.nn.Parameter(w2_input_scale,
+                                                          requires_grad=False)
+
+        # Fp8 moe kernel needs single weight scale for w13 per expert.
+        # We take the max then dequant and requant each expert.
+        assert layer.w13_weight_scale is not None
+        shard_size = layer.intermediate_size_per_partition
+        max_w13_scales = layer.w13_weight_scale.max(dim=1).values
+        for expert_id in range(layer.num_experts):
+            start = 0
+            for shard_id in range(2):
+                dq_weight = per_tensor_dequantize(
+                    layer.w13_weight[expert_id][start:start + shard_size, :],
+                    layer.w13_weight_scale[expert_id][shard_id])
+                layer.w13_weight[expert_id][
+                    start:start + shard_size, :], _ = ops.scaled_fp8_quant(
+                        dq_weight, max_w13_scales[expert_id])
+                start += shard_size
+
+        layer.w13_weight_scale = torch.nn.Parameter(max_w13_scales,
+                                                    requires_grad=False)
+
+    def apply(
+        self,
+        layer: torch.nn.Module,
+        x: torch.Tensor,
+        router_logits: torch.Tensor,
+        top_k: int,
+        renormalize: bool = True,
+        use_grouped_topk: bool = False,
+        num_expert_group: Optional[int] = None,
+        topk_group: Optional[int] = None,
+        custom_routing_function: Optional[Callable] = None,
+    ) -> torch.Tensor:
+
+        from vllm.model_executor.layers.fused_moe import fused_experts
+
+        topk_weights, topk_ids = FusedMoE.select_experts(
+            hidden_states=x,
+            router_logits=router_logits,
+            use_grouped_topk=use_grouped_topk,
+            top_k=top_k,
+            renormalize=renormalize,
+            topk_group=topk_group,
+            num_expert_group=num_expert_group,
+            custom_routing_function=custom_routing_function)
+
+        return fused_experts(x,
+                             layer.w13_weight,
+                             layer.w2_weight,
+                             topk_weights=topk_weights,
+                             topk_ids=topk_ids,
+                             inplace=True,
+                             use_fp8_w8a8=True,
+                             w1_scale=layer.w13_weight_scale,
+                             w2_scale=layer.w2_weight_scale,
+                             a1_scale=layer.w13_input_scale,
+                             a2_scale=layer.w2_input_scale)
+
+
+class CompressedTensorsWNA16MoEMethod(CompressedTensorsMoEMethod):
+
+    def __init__(
+            self,
+            quant_config: "CompressedTensorsConfig"  # type: ignore # noqa E501
+    ):
+        self.quant_config = quant_config
+        # TODO: @dsikka: refactor this to use schemes as other kernels
+        # are supported + check if the layer is being ignored.
+        config = self.quant_config.target_scheme_map["Linear"].get("weights")
+        self.num_bits = config.num_bits
+        self.packed_factor = 32 // config.num_bits
+        self.strategy = config.strategy.value
+        self.group_size = config.group_size
+        assert config.symmetric, (
+            "Only symmetric quantization is supported for MoE")
+
+        if not (self.quant_config.quant_format
+                == CompressionFormat.pack_quantized.value
+                and self.num_bits in WNA16_SUPPORTED_BITS):
+            raise ValueError("For Fused MoE layers, only ",
+                             f"{CompressionFormat.pack_quantized.value} ",
+                             "is supported for the following bits: ",
+                             f"{WNA16_SUPPORTED_BITS}")
+
+    def create_weights(self, layer: torch.nn.Module, num_experts: int,
+                       hidden_size: int, intermediate_size: int,
+                       params_dtype: torch.dtype, **extra_weight_attrs):
+
+        # Will transpose the loaded weight along the
+        # intermediate and hidden dim sizes. Will
+        # shard for TP along the transposed dims
+        extra_weight_attrs.update({
+            "is_transposed": True,
+            "quant_method": self.strategy
+        })
+        w13_weight = torch.nn.Parameter(torch.empty(num_experts,
+                                                    hidden_size //
+                                                    self.packed_factor,
+                                                    2 * intermediate_size,
+                                                    dtype=torch.int32),
+                                        requires_grad=False)
+        layer.register_parameter("w13_weight_packed", w13_weight)
+        set_weight_attrs(w13_weight, extra_weight_attrs)
+
+        w2_weight = torch.nn.Parameter(torch.empty(num_experts,
+                                                   intermediate_size //
+                                                   self.packed_factor,
+                                                   hidden_size,
+                                                   dtype=torch.int32),
+                                       requires_grad=False)
+        layer.register_parameter("w2_weight_packed", w2_weight)
+        set_weight_attrs(w2_weight, extra_weight_attrs)
+
+        if self.strategy == "channel":
+            num_groups_w2 = num_groups_w13 = 1
+            self.group_size = -1
+        else:
+            num_groups_w2 = intermediate_size // self.group_size
+            num_groups_w13 = hidden_size // self.group_size
+
+        w13_scale = torch.nn.Parameter(torch.ones(num_experts,
+                                                  num_groups_w13,
+                                                  2 * intermediate_size,
+                                                  dtype=params_dtype),
+                                       requires_grad=False)
+        layer.register_parameter("w13_weight_scale", w13_scale)
+        set_weight_attrs(w13_scale, extra_weight_attrs)
+
+        w2_scale = torch.nn.Parameter(torch.ones(num_experts,
+                                                 num_groups_w2,
+                                                 hidden_size,
+                                                 dtype=params_dtype),
+                                      requires_grad=False)
+        layer.register_parameter("w2_weight_scale", w2_scale)
+        set_weight_attrs(w2_scale, extra_weight_attrs)
+
+        w2_weight_shape = torch.nn.Parameter(torch.empty(num_experts, 2),
+                                             requires_grad=False)
+        layer.register_parameter("w2_weight_shape", w2_weight_shape)
+        set_weight_attrs(w2_weight_shape, extra_weight_attrs)
+        w13_weight_shape = torch.nn.Parameter(torch.empty(num_experts, 2),
+                                              requires_grad=False)
+
+        layer.register_parameter("w13_weight_shape", w13_weight_shape)
+        set_weight_attrs(w13_weight_shape, extra_weight_attrs)
+
+        w13_g_idx = torch.nn.Parameter(
+            torch.empty(
+                num_experts,
+                hidden_size,
+                dtype=torch.int32,
+            ),
+            requires_grad=False,
+        )
+        layer.register_parameter("w13_g_idx", w13_g_idx)
+        set_weight_attrs(w13_g_idx, extra_weight_attrs)
+
+        w2_g_idx = torch.nn.Parameter(
+            torch.empty(
+                num_experts,
+                intermediate_size,
+                dtype=torch.int32,
+            ),
+            requires_grad=False,
+        )
+        layer.register_parameter("w2_g_idx", w2_g_idx)
+        set_weight_attrs(w2_g_idx, extra_weight_attrs)
+
+        w13_g_idx_sort_indices = torch.nn.Parameter(
+            torch.empty(
+                num_experts,
+                hidden_size,
+                dtype=torch.int32,
+            ),
+            requires_grad=False,
+        )
+        layer.register_parameter("w13_g_idx_sort_indices",
+                                 w13_g_idx_sort_indices)
+        set_weight_attrs(w13_g_idx_sort_indices, extra_weight_attrs)
+
+        w2_g_idx_sort_indices = torch.nn.Parameter(
+            torch.empty(
+                num_experts,
+                intermediate_size,
+                dtype=torch.int32,
+            ),
+            requires_grad=False,
+        )
+        layer.register_parameter("w2_g_idx_sort_indices",
+                                 w2_g_idx_sort_indices)
+        set_weight_attrs(w2_g_idx_sort_indices, extra_weight_attrs)
+
+        layer.a13_scale = None
+        layer.a2_scale = None
+        layer.marlin_state = GPTQMarlinState.REPACK
+
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+
+        def replace_tensor(name, new_t):
+            # It is important to use resize_() here since it ensures
+            # the same buffer is reused
+            getattr(layer, name).resize_(new_t.shape)
+            getattr(layer, name).copy_(new_t)
+            del new_t
+
+        def get_scale_perms(num_bits: int):
+            scale_perm: List[int] = []
+            for i in range(8):
+                scale_perm.extend([i + 8 * j for j in range(8)])
+            scale_perm_single: List[int] = []
+            for i in range(4):
+                scale_perm_single.extend(
+                    [2 * i + j for j in [0, 1, 8, 9, 16, 17, 24, 25]])
+            return scale_perm, scale_perm_single
+
+        def marlin_permute_scales(s: torch.Tensor, size_k: int, size_n: int,
+                                  group_size: int, num_bits: int):
+            scale_perm, scale_perm_single = get_scale_perms(num_bits)
+            if group_size < size_k and group_size != -1:
+                s = s.reshape((-1, len(scale_perm)))[:, scale_perm]
+            else:
+                s = s.reshape((-1, len(scale_perm_single)))[:,
+                                                            scale_perm_single]
+            s = s.reshape((-1, size_n)).contiguous()
+            return s
+
+        def marlin_moe_permute_scales(s: torch.Tensor, size_k: int,
+                                      size_n: int, group_size: int,
+                                      num_bits: int):
+            num_experts = s.shape[0]
+            output = torch.empty((num_experts, s.shape[1], s.shape[2]),
+                                 device=s.device,
+                                 dtype=s.dtype)
+            for e in range(num_experts):
+                output[e] = marlin_permute_scales(s[e], size_k, size_n,
+                                                  group_size, num_bits)
+            return output
+
+        size_k2 = layer.w2_weight_packed.shape[2]
+        size_k13 = layer.w13_weight_packed.shape[2]
+
+        num_experts = layer.w13_g_idx.shape[0]
+        device = layer.w13_g_idx.device
+        layer.w13_g_idx = torch.nn.Parameter(
+            torch.empty((num_experts, 0), dtype=torch.int32, device=device),
+            requires_grad=False,
+        )
+        layer.w2_g_idx = torch.nn.Parameter(
+            torch.empty((num_experts, 0), dtype=torch.int32, device=device),
+            requires_grad=False,
+        )
+        layer.w13_g_idx_sort_indices = torch.nn.Parameter(
+            torch.empty((num_experts, 0), dtype=torch.int32, device=device),
+            requires_grad=False,
+        )
+        layer.w2_g_idx_sort_indices = torch.nn.Parameter(
+            torch.empty((num_experts, 0), dtype=torch.int32, device=device),
+            requires_grad=False,
+        )
+
+        marlin_w13_qweight = ops.gptq_marlin_moe_repack(
+            layer.w13_weight_packed,
+            layer.w13_g_idx_sort_indices,
+            layer.w13_weight_packed.shape[1] * self.packed_factor,
+            layer.w13_weight_packed.shape[2],
+            self.num_bits,
+        )
+        replace_tensor("w13_weight_packed", marlin_w13_qweight)
+        marlin_w2_qweight = ops.gptq_marlin_moe_repack(
+            layer.w2_weight_packed,
+            layer.w2_g_idx_sort_indices,
+            layer.w2_weight_packed.shape[1] * self.packed_factor,
+            layer.w2_weight_packed.shape[2],
+            self.num_bits,
+        )
+        replace_tensor("w2_weight_packed", marlin_w2_qweight)
+        # Repack scales
+        marlin_w13_scales = marlin_moe_permute_scales(
+            layer.w13_weight_scale,
+            size_k13,
+            layer.w13_weight_scale.shape[2],
+            self.group_size,
+            self.num_bits,
+        )
+        replace_tensor("w13_weight_scale", marlin_w13_scales)
+        marlin_w2_scales = marlin_moe_permute_scales(
+            layer.w2_weight_scale,
+            layer.w2_weight_scale.shape[1] * self.packed_factor,
+            size_k2,
+            self.group_size,
+            self.num_bits,
+        )
+        replace_tensor("w2_weight_scale", marlin_w2_scales)
+
+    def apply(
+        self,
+        layer: torch.nn.Module,
+        x: torch.Tensor,
+        router_logits: torch.Tensor,
+        top_k: int,
+        renormalize: bool = True,
+        use_grouped_topk: bool = False,
+        num_expert_group: Optional[int] = None,
+        topk_group: Optional[int] = None,
+        custom_routing_function: Optional[Callable] = None,
+    ) -> torch.Tensor:
+
+        from vllm.model_executor.layers.fused_moe.fused_marlin_moe import (
+            fused_marlin_moe)
+
+        topk_weights, topk_ids = FusedMoE.select_experts(
+            hidden_states=x,
+            router_logits=router_logits,
+            use_grouped_topk=use_grouped_topk,
+            top_k=top_k,
+            renormalize=renormalize,
+            topk_group=topk_group,
+            num_expert_group=num_expert_group,
+            custom_routing_function=custom_routing_function)
+
+        return fused_marlin_moe(
+            x,
+            layer.w13_weight_packed,
+            layer.w2_weight_packed,
+            layer.w13_weight_scale,
+            layer.w2_weight_scale,
+            router_logits,
+            topk_weights,
+            topk_ids,
+            g_idx1=layer.w13_g_idx,
+            g_idx2=layer.w2_g_idx,
+            sort_indices1=layer.w13_g_idx_sort_indices,
+            sort_indices2=layer.w2_g_idx_sort_indices,
+            num_bits=self.num_bits,
+        )

vllm/model_executor/layers/quantization/compressed_tensors/schemes/__init__.py

@@ -0,0 +1,19 @@
+from .compressed_tensors_scheme import CompressedTensorsScheme
+from .compressed_tensors_w4a16_24 import (W4A16SPARSE24_SUPPORTED_BITS,
+                                          CompressedTensorsW4A16Sparse24)
+from .compressed_tensors_w8a8_fp8 import CompressedTensorsW8A8Fp8
+from .compressed_tensors_w8a8_int8 import CompressedTensorsW8A8Int8
+from .compressed_tensors_w8a16_fp8 import CompressedTensorsW8A16Fp8
+from .compressed_tensors_wNa16 import (WNA16_SUPPORTED_BITS,
+                                       CompressedTensorsWNA16)
+
+__all__ = [
+    "CompressedTensorsScheme",
+    "CompressedTensorsWNA16",
+    "CompressedTensorsW8A16Fp8",
+    "CompressedTensorsW4A16Sparse24",
+    "CompressedTensorsW8A8Int8",
+    "CompressedTensorsW8A8Fp8",
+    "WNA16_SUPPORTED_BITS",
+    "W4A16SPARSE24_SUPPORTED_BITS",
+]

vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_scheme.py

@@ -0,0 +1,52 @@
+from abc import ABC, abstractmethod
+from typing import Optional
+
+import torch
+
+__all__ = ["CompressedTensorsScheme"]
+
+
+class CompressedTensorsScheme(ABC):
+    """
+    Abstract class used to describe the weight creation and forward pass 
+    of different quantization schemes supported by CompressedTensors.
+    """
+
+    @classmethod
+    @abstractmethod
+    def get_min_capability(cls) -> int:
+        """
+        Get minimum device capability.
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def create_weights(self, *args, **kwargs):
+        """
+        Weight creation for the particular scheme. Inputs to this function 
+
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def apply_weights(self, layer: torch.nn.Module, x: torch.Tensor,
+                      bias: Optional[torch.Tensor]):
+        """
+        Run the forward pass for the particular scheme. This is where 
+        scheme-specific dequant/quant steps/kernels should be applied.
+
+        :param layer: torch.nn.Module with the registered weights and 
+            other parameters relevant to the particular scheme. 
+        :param x: input to the layer
+        :param bias: bias parameter
+
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def process_weights_after_loading(self, layer: torch.nn.Module):
+        """
+        Called after weight loading is complete for any cleanup that
+        needs to occur.
+        """
+        raise NotImplementedError

vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w4a16_24.py

@@ -0,0 +1,153 @@
+from typing import Callable, List, Optional
+
+import torch
+from torch.nn import Parameter
+
+from vllm import _custom_ops as ops
+from vllm.model_executor.layers.quantization.compressed_tensors.schemes import (
+    CompressedTensorsScheme)
+from vllm.model_executor.layers.quantization.gptq_marlin_24 import (
+    GPTQ_MARLIN_24_MAX_PARALLEL, GPTQ_MARLIN_24_MIN_THREAD_N)
+from vllm.model_executor.parameter import (BasevLLMParameter,
+                                           ChannelQuantScaleParameter,
+                                           GroupQuantScaleParameter,
+                                           PackedvLLMParameter)
+from vllm.scalar_type import scalar_types
+
+__all__ = ["CompressedTensorsW4A16Sparse24"]
+W4A16SPARSE24_SUPPORTED_TYPES_MAP = {
+    4: scalar_types.uint4b8,
+}
+W4A16SPARSE24_SUPPORTED_BITS = list(W4A16SPARSE24_SUPPORTED_TYPES_MAP.keys())
+
+
+class CompressedTensorsW4A16Sparse24(CompressedTensorsScheme):
+
+    def __init__(self,
+                 strategy: str,
+                 num_bits: int,
+                 group_size: Optional[int] = None):
+        self.strategy = strategy
+        self.group_size = group_size
+        self.tile_size = 16
+
+        if num_bits not in W4A16SPARSE24_SUPPORTED_TYPES_MAP:
+            raise ValueError(
+                f"Unsupported num_bits = {num_bits}. "
+                f"Supported num_bits = {W4A16SPARSE24_SUPPORTED_BITS}")
+
+        self.quant_type = W4A16SPARSE24_SUPPORTED_TYPES_MAP[num_bits]
+
+        if self.strategy == "group" and self.group_size is None:
+            raise ValueError(
+                "group_size must be given when using strategy group")
+
+    @classmethod
+    def get_min_capability(cls) -> int:
+        # ampere + up
+        return 80
+
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+        # required by torch.compile to be torch.nn.Parameter
+        layer.weight_packed = Parameter(layer.weight_packed.data,
+                                        requires_grad=False)
+        layer.scale_packed = Parameter(layer.scale_packed.data,
+                                       requires_grad=False)
+        layer.meta = Parameter(layer.meta.data, requires_grad=False)
+
+    def create_weights(self, layer: torch.nn.Module, input_size: int,
+                       output_partition_sizes: List[int],
+                       input_size_per_partition: int,
+                       params_dtype: torch.dtype, weight_loader: Callable,
+                       **kwargs):
+
+        pack_factor = 32 // self.quant_type.size_bits
+        output_size_per_partition = sum(output_partition_sizes)
+
+        qweight = PackedvLLMParameter(data=torch.empty(
+            input_size_per_partition // self.tile_size // 2,
+            output_size_per_partition * self.tile_size // pack_factor,
+            dtype=torch.int32,
+        ),
+                                      input_dim=0,
+                                      output_dim=1,
+                                      packed_dim=1,
+                                      packed_factor=pack_factor,
+                                      marlin_tile_size=self.tile_size,
+                                      weight_loader=weight_loader)
+
+        input_groups = (1 if self.group_size is None else
+                        input_size_per_partition // self.group_size)
+
+        weight_scale_args = {
+            "data":
+            torch.empty(
+                input_groups,
+                output_size_per_partition,
+                dtype=params_dtype,
+            ),
+            "weight_loader":
+            weight_loader
+        }
+
+        if self.group_size is not None:
+            scales = GroupQuantScaleParameter(output_dim=1,
+                                              input_dim=0,
+                                              **weight_scale_args)
+        else:
+            scales = ChannelQuantScaleParameter(output_dim=1,
+                                                **weight_scale_args)
+
+        weight_shape = BasevLLMParameter(data=torch.empty(2,
+                                                          dtype=torch.int64),
+                                         weight_loader=weight_loader)
+
+        meta = PackedvLLMParameter(data=torch.empty(
+            input_size_per_partition // 8 // 2 // 2,
+            output_size_per_partition * 2,
+            dtype=torch.int16,
+        ),
+                                   input_dim=0,
+                                   output_dim=1,
+                                   packed_dim=1,
+                                   packed_factor=1,
+                                   marlin_tile_size=2,
+                                   weight_loader=weight_loader)
+
+        layer.register_parameter("weight_packed", qweight)
+        layer.register_parameter("weight_shape", weight_shape)
+        layer.register_parameter("scale_packed", scales)
+        layer.register_parameter("meta", meta)
+
+        max_workspace_size = (
+            output_size_per_partition //
+            GPTQ_MARLIN_24_MIN_THREAD_N) * GPTQ_MARLIN_24_MAX_PARALLEL
+
+        workspace = Parameter(torch.zeros(max_workspace_size, dtype=torch.int),
+                              requires_grad=False)
+        layer.workspace = workspace
+
+    def apply_weights(self, layer: torch.nn.Module, x: torch.Tensor,
+                      bias: Optional[torch.Tensor]) -> torch.Tensor:
+
+        qweight = layer.weight_packed
+        meta = layer.meta
+        scales = layer.scale_packed
+        workspace = layer.workspace
+
+        x_2d = x.view(-1, x.shape[-1])
+
+        size_m = x_2d.shape[0]
+        size_k = x_2d.shape[1]
+        size_n = scales.shape[1]
+
+        output_2d = ops.gptq_marlin_24_gemm(x_2d, qweight, meta, scales,
+                                            workspace, self.quant_type, size_m,
+                                            size_n, size_k)
+
+        output = output_2d.view(x.shape[:-1] + (output_2d.shape[1], ))
+
+        if bias is not None:
+            output.add_(bias)  # In-place add
+
+        return output

vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a16_fp8.py

@@ -0,0 +1,118 @@
+from typing import Callable, List, Optional
+
+import torch
+
+from vllm.model_executor.layers.quantization.compressed_tensors.schemes import (
+    CompressedTensorsScheme)
+from vllm.model_executor.layers.quantization.compressed_tensors.utils import (
+    QuantizationStrategy)
+from vllm.model_executor.layers.quantization.utils.marlin_utils_fp8 import (
+    apply_fp8_marlin_linear, prepare_fp8_layer_for_marlin)
+from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
+    convert_to_channelwise)
+from vllm.model_executor.parameter import (ChannelQuantScaleParameter,
+                                           ModelWeightParameter,
+                                           PerTensorScaleParameter)
+
+__all__ = ["CompressedTensorsW8A16Fp8"]
+
+SUPPORTED_STRATEGIES = [
+    QuantizationStrategy.CHANNEL, QuantizationStrategy.TENSOR
+]
+
+
+class CompressedTensorsW8A16Fp8(CompressedTensorsScheme):
+
+    def __init__(self, strategy: str, is_static_input_scheme: bool):
+        self.strategy = strategy
+        self.is_static_input_scheme = is_static_input_scheme
+
+    @classmethod
+    def get_min_capability(cls) -> int:
+        # ampere and up
+        return 80
+
+    # W8A8-Fp8 kernels support only per-tensor and per-channel cases.
+    # So if we have a fused module (QKV, MLP) with per tensor scales,
+    # we expand each scale to its shard's channels.
+    def process_weights_after_loading(self, layer) -> None:
+        if self.strategy == QuantizationStrategy.TENSOR:
+            ws_channelwise = convert_to_channelwise(layer.weight_scale,
+                                                    layer.logical_widths)
+            layer.weight_scale = torch.nn.Parameter(ws_channelwise,
+                                                    requires_grad=False)
+        else:
+            # required by torch.compile to be torch.nn.Parameter
+            layer.weight_scale = torch.nn.Parameter(layer.weight_scale.data,
+                                                    requires_grad=False)
+
+        # Weights must be transposed for marlin
+        layer.weight = torch.nn.Parameter(layer.weight.t(),
+                                          requires_grad=False)
+
+        if self.is_static_input_scheme:
+            # required by torch.compile to be torch.nn.Parameter
+            layer.input_scale = torch.nn.Parameter(layer.input_scale.data,
+                                                   requires_grad=False)
+        prepare_fp8_layer_for_marlin(layer, strategy="channel")
+
+    def create_weights(self, layer: torch.nn.Module, input_size: int,
+                       output_partition_sizes: List[int],
+                       input_size_per_partition: int,
+                       params_dtype: torch.dtype, weight_loader: Callable,
+                       **kwargs):
+
+        output_size_per_partition = sum(output_partition_sizes)
+        layer.logical_widths = output_partition_sizes
+        layer.input_size_per_partition = input_size_per_partition
+        layer.output_size_per_partition = output_size_per_partition
+        layer.orig_dtype = params_dtype
+
+        # WEIGHT
+        weight = ModelWeightParameter(data=torch.empty(
+            output_size_per_partition,
+            input_size_per_partition,
+            dtype=torch.float8_e4m3fn),
+                                      input_dim=1,
+                                      output_dim=0,
+                                      weight_loader=weight_loader)
+        layer.register_parameter("weight", weight)
+
+        # WEIGHT SCALE
+        if self.strategy == QuantizationStrategy.CHANNEL:
+            weight_scale = ChannelQuantScaleParameter(
+                data=torch.empty((sum(output_partition_sizes), 1),
+                                 dtype=torch.float32),
+                output_dim=0,
+                weight_loader=weight_loader)
+        elif self.strategy == QuantizationStrategy.TENSOR:
+            weight_scale = PerTensorScaleParameter(data=torch.empty(
+                len(output_partition_sizes), dtype=torch.float32),
+                                                   weight_loader=weight_loader)
+        else:
+            raise ValueError(
+                f"Unsupported weight strategy={self.strategy}, "
+                f"supported strategies are {SUPPORTED_STRATEGIES}")
+
+        weight_scale[:] = torch.finfo(torch.float32).min
+        layer.register_parameter("weight_scale", weight_scale)
+
+        # INPUT SCALE (to deal with converted checkpoints)
+        if self.is_static_input_scheme:
+            input_scale = PerTensorScaleParameter(data=torch.empty(
+                len(output_partition_sizes), dtype=torch.float32),
+                                                  weight_loader=weight_loader)
+            layer.register_parameter("input_scale", input_scale)
+
+    def apply_weights(self,
+                      layer: torch.nn.Module,
+                      x: torch.Tensor,
+                      bias: Optional[torch.Tensor] = None) -> torch.Tensor:
+
+        return apply_fp8_marlin_linear(input=x,
+                                       weight=layer.weight,
+                                       weight_scale=layer.weight_scale,
+                                       workspace=layer.workspace,
+                                       size_n=layer.output_size_per_partition,
+                                       size_k=layer.input_size_per_partition,
+                                       bias=bias)

vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_fp8.py

@@ -0,0 +1,143 @@
+from typing import Callable, List, Optional
+
+import torch
+from torch.nn import Parameter
+
+from vllm.model_executor.layers.quantization.compressed_tensors.schemes import (
+    CompressedTensorsScheme)
+from vllm.model_executor.layers.quantization.compressed_tensors.utils import (
+    QuantizationStrategy)
+from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
+    apply_fp8_linear, cutlass_fp8_supported, normalize_e4m3fn_to_e4m3fnuz,
+    requantize_with_max_scale)
+from vllm.model_executor.parameter import (ChannelQuantScaleParameter,
+                                           ModelWeightParameter,
+                                           PerTensorScaleParameter)
+from vllm.utils import is_hip
+
+__all__ = ["CompressedTensorsW8A8Fp8"]
+
+
+class CompressedTensorsW8A8Fp8(CompressedTensorsScheme):
+
+    def __init__(self, strategy: str, is_static_input_scheme: bool):
+        self.strategy = strategy
+        self.is_static_input_scheme = is_static_input_scheme
+        self.cutlass_fp8_supported = cutlass_fp8_supported()
+
+    @classmethod
+    def get_min_capability(cls) -> int:
+        # lovelace and up
+        return 89
+
+    def process_weights_after_loading(self, layer) -> None:
+        # If per tensor, when we have a fused module (e.g. QKV) with per
+        # tensor scales (thus N scales being passed to the kernel),
+        # requantize so we can always run per tensor
+        if self.strategy == QuantizationStrategy.TENSOR:
+            max_w_scale, weight = requantize_with_max_scale(
+                weight=layer.weight,
+                weight_scale=layer.weight_scale,
+                logical_widths=layer.logical_widths,
+            )
+
+            if is_hip():
+                weight, max_w_scale, input_scale = normalize_e4m3fn_to_e4m3fnuz(
+                    weight=weight,
+                    weight_scale=max_w_scale,
+                    input_scale=layer.input_scale)
+                if input_scale is not None:
+                    layer.input_scale = Parameter(input_scale,
+                                                  requires_grad=False)
+
+            layer.weight = Parameter(weight.t(), requires_grad=False)
+            layer.weight_scale = Parameter(max_w_scale, requires_grad=False)
+
+        # If channelwise, scales are already lined up, so just transpose.
+        elif self.strategy == QuantizationStrategy.CHANNEL:
+            weight = layer.weight
+
+            if is_hip():
+                weight, weight_scale, input_scale = \
+                    normalize_e4m3fn_to_e4m3fnuz(
+                        weight=weight,
+                        weight_scale=layer.weight_scale,
+                        input_scale=layer.input_scale)
+                if input_scale is not None:
+                    layer.input_scale = Parameter(input_scale,
+                                                  requires_grad=False)
+            else:
+                weight_scale = layer.weight_scale.data
+
+            layer.weight = Parameter(weight.t(), requires_grad=False)
+            # required by torch.compile to be torch.nn.Parameter
+            layer.weight_scale = Parameter(weight_scale, requires_grad=False)
+
+        else:
+            raise ValueError(f"Unknown quantization strategy {self.strategy}")
+
+        # INPUT SCALE
+        if self.is_static_input_scheme:
+            layer.input_scale = Parameter(layer.input_scale.max(),
+                                          requires_grad=False)
+        else:
+            layer.input_scale = None
+
+    def create_weights(self, layer: torch.nn.Module,
+                       output_partition_sizes: List[int],
+                       input_size_per_partition: int,
+                       params_dtype: torch.dtype, weight_loader: Callable,
+                       **kwargs):
+        output_size_per_partition = sum(output_partition_sizes)
+        layer.logical_widths = output_partition_sizes
+
+        # WEIGHT
+        weight = ModelWeightParameter(data=torch.empty(
+            output_size_per_partition,
+            input_size_per_partition,
+            dtype=torch.float8_e4m3fn),
+                                      input_dim=1,
+                                      output_dim=0,
+                                      weight_loader=weight_loader)
+        layer.register_parameter("weight", weight)
+
+        # WEIGHT SCALE
+        # TODO: update create_xxx_parameter functions to return
+        # the newly added parameters
+        if self.strategy == QuantizationStrategy.CHANNEL:
+            weight_scale = ChannelQuantScaleParameter(
+                data=torch.empty((sum(output_partition_sizes), 1),
+                                 dtype=torch.float32),
+                output_dim=0,
+                weight_loader=weight_loader)
+        else:
+            assert self.strategy == QuantizationStrategy.TENSOR
+            weight_scale = PerTensorScaleParameter(data=torch.empty(
+                len(output_partition_sizes), dtype=torch.float32),
+                                                   weight_loader=weight_loader)
+
+        # min requirement for fp8 kernels
+        weight_scale[:] = torch.finfo(torch.float32).min
+        layer.register_parameter("weight_scale", weight_scale)
+
+        # INPUT SCALE
+        if self.is_static_input_scheme:
+            input_scale = PerTensorScaleParameter(data=torch.empty(
+                len(output_partition_sizes), dtype=torch.float32),
+                                                  weight_loader=weight_loader)
+            input_scale[:] = torch.finfo(torch.float32).min
+            layer.register_parameter("input_scale", input_scale)
+
+    def apply_weights(self,
+                      layer: torch.nn.Module,
+                      x: torch.Tensor,
+                      bias: Optional[torch.Tensor] = None) -> torch.Tensor:
+
+        return apply_fp8_linear(
+            input=x,
+            weight=layer.weight,
+            weight_scale=layer.weight_scale,
+            input_scale=layer.input_scale,
+            bias=bias,
+            cutlass_fp8_supported=self.cutlass_fp8_supported,
+            use_per_token_if_dynamic=True)

vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_int8.py

@@ -0,0 +1,155 @@
+from typing import Callable, List, Optional
+
+import torch
+from torch.nn import Parameter
+
+from vllm.logger import init_logger
+from vllm.model_executor.layers.quantization.compressed_tensors.schemes import (
+    CompressedTensorsScheme)
+from vllm.model_executor.layers.quantization.compressed_tensors.utils import (
+    QuantizationStrategy)
+from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
+    apply_int8_linear, convert_to_channelwise)
+from vllm.model_executor.parameter import (BasevLLMParameter,
+                                           ChannelQuantScaleParameter,
+                                           ModelWeightParameter,
+                                           PerTensorScaleParameter)
+
+logger = init_logger(__name__)
+
+
+class CompressedTensorsW8A8Int8(CompressedTensorsScheme):
+
+    def __init__(self, strategy: str, is_static_input_scheme: bool,
+                 input_symmetric: bool):
+        self.strategy = strategy
+        self.is_static_input_scheme = is_static_input_scheme
+        self.input_symmetric = input_symmetric
+
+    @classmethod
+    def get_min_capability(cls) -> int:
+        # turing and up
+        return 75
+
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+        # WEIGHT
+        # Cutlass kernels need transposed weight.
+        weight = layer.weight
+        layer.weight = Parameter(weight.t(), requires_grad=False)
+
+        # WEIGHT SCALE
+        # Cutlass kernels support only per-tensor and per-channel.
+        # If we have a fused module (QKV, MLP) with per tensor scales (thus N
+        # scales being passed to the kernel), convert to the per-channel case.
+        is_fused_module = len(self.logical_widths) > 1
+        if is_fused_module and self.strategy == QuantizationStrategy.TENSOR:
+            ws_channelwise = convert_to_channelwise(layer.weight_scale,
+                                                    self.logical_widths)
+            layer.weight_scale = Parameter(ws_channelwise, requires_grad=False)
+        else:
+            layer.weight_scale = Parameter(layer.weight_scale.data,
+                                           requires_grad=False)
+        # INPUT SCALE
+        if self.is_static_input_scheme:
+            if self.input_symmetric:
+                layer.input_scale = Parameter(layer.input_scale.max(),
+                                              requires_grad=False)
+                layer.input_zero_point = None
+            else:
+                # reconstruct the ranges
+                int8_traits = torch.iinfo(torch.int8)
+                azps = layer.input_zero_point.to(dtype=torch.int32)
+                range_max = (layer.input_scale *
+                             (int8_traits.max - azps)).max()
+                range_min = (layer.input_scale *
+                             (int8_traits.min - azps)).min()
+
+                scale = (range_max - range_min) / (int8_traits.max -
+                                                   int8_traits.min)
+                layer.input_scale = Parameter(scale, requires_grad=False)
+
+                # AZP loaded as int8 but used as int32
+                azp = (int8_traits.min -
+                       range_min / scale).to(dtype=torch.int32)
+                layer.input_zero_point = Parameter(azp, requires_grad=False)
+
+        else:
+            layer.input_scale = None
+            layer.input_zero_point = None
+
+        # azp_adj is the AZP adjustment term, used to account for weights.
+        # It does not depend on scales or azp, so it is the same for
+        # static and dynamic quantization.
+        # For more details, see csrc/quantization/cutlass_w8a8/Epilogues.md
+        # https://github.com/vllm-project/vllm/blob/8d59dbb00044a588cab96bcdc028006ed922eb06/csrc/quantization/cutlass_w8a8/Epilogues.md
+        if not self.input_symmetric:
+            layer.azp_adj = layer.weight.sum(dim=0,
+                                             keepdim=True,
+                                             dtype=torch.int32)
+        else:
+            layer.azp_adj = None
+
+    def create_weights(self, layer: torch.nn.Module,
+                       output_partition_sizes: List[int],
+                       input_size_per_partition: int,
+                       params_dtype: torch.dtype, weight_loader: Callable,
+                       **kwargs):
+        self.logical_widths = output_partition_sizes
+
+        # WEIGHT
+        if input_size_per_partition % 64 != 0:
+            pad_input_size_per_partition = (input_size_per_partition // 64 + 1) * 64
+        else:
+            pad_input_size_per_partition = input_size_per_partition
+        w_pad = torch.zeros(
+                sum(output_partition_sizes),
+                pad_input_size_per_partition,
+                dtype=torch.int8)
+        w = w_pad[:, :input_size_per_partition]
+        weight = ModelWeightParameter(data=w,
+                                      input_dim=1,
+                                      output_dim=0,
+                                      weight_loader=weight_loader)
+
+        layer.register_parameter("weight", weight)
+
+        # WEIGHT SCALE
+        if self.strategy == QuantizationStrategy.CHANNEL:
+            weight_scale = ChannelQuantScaleParameter(
+                data=torch.empty((sum(output_partition_sizes), 1),
+                                 dtype=torch.float32),
+                output_dim=0,
+                weight_loader=weight_loader)
+        else:
+            assert self.strategy == QuantizationStrategy.TENSOR
+            weight_scale = PerTensorScaleParameter(data=torch.empty(
+                len(output_partition_sizes), dtype=torch.float32),
+                                                   weight_loader=weight_loader)
+        layer.register_parameter("weight_scale", weight_scale)
+
+        # INPUT SCALE
+        if self.is_static_input_scheme:
+            input_scale = BasevLLMParameter(data=torch.empty(
+                1, dtype=torch.float32),
+                                            weight_loader=weight_loader)
+            layer.register_parameter("input_scale", input_scale)
+
+            if not self.input_symmetric:
+                # Note: compressed-tensors stores the zp using the same dtype
+                # as the weights
+                # AZP loaded as int8 but used as int32
+                input_zero_point = BasevLLMParameter(
+                    data=torch.empty(1, dtype=torch.int8),
+                    weight_loader=weight_loader)
+                layer.register_parameter("input_zero_point", input_zero_point)
+
+    def apply_weights(self, layer: torch.nn.Module, x: torch.Tensor,
+                      bias: Optional[torch.Tensor]) -> torch.Tensor:
+
+        return apply_int8_linear(input=x,
+                                 weight=layer.weight,
+                                 weight_scale=layer.weight_scale,
+                                 input_scale=layer.input_scale,
+                                 input_zero_point=layer.input_zero_point,
+                                 azp_adj=layer.azp_adj,
+                                 bias=bias)

vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_wNa16.py

@@ -0,0 +1,163 @@
+from typing import Callable, List, Optional, Set
+
+import torch
+
+from vllm.logger import init_logger
+from vllm.model_executor.layers.quantization.compressed_tensors.schemes import (
+    CompressedTensorsScheme)
+from vllm.model_executor.layers.quantization.compressed_tensors.utils import (
+    ActivationOrdering)
+from vllm.model_executor.layers.quantization.kernels import (
+    MPLinearLayerConfig, choose_mp_linear_kernel)
+from vllm.model_executor.layers.quantization.utils.marlin_utils import (
+    marlin_repeat_scales_on_all_ranks)
+from vllm.model_executor.parameter import (BasevLLMParameter,
+                                           ChannelQuantScaleParameter,
+                                           GroupQuantScaleParameter,
+                                           PackedvLLMParameter,
+                                           RowvLLMParameter)
+from vllm.scalar_type import scalar_types
+
+logger = init_logger(__name__)
+
+__all__ = ["CompressedTensorsWNA16"]
+WNA16_SUPPORTED_TYPES_MAP = {
+    4: scalar_types.uint4b8,
+    8: scalar_types.uint8b128
+}
+WNA16_SUPPORTED_BITS = list(WNA16_SUPPORTED_TYPES_MAP.keys())
+
+
+class CompressedTensorsWNA16(CompressedTensorsScheme):
+    _kernel_backends_being_used: Set[str] = set()
+
+    def __init__(self,
+                 strategy: str,
+                 num_bits: int,
+                 group_size: Optional[int] = None,
+                 actorder: Optional[ActivationOrdering] = None):
+
+        self.pack_factor = 32 // num_bits
+        self.strategy = strategy
+        self.group_size = -1 if group_size is None else group_size
+        self.has_g_idx = actorder == ActivationOrdering.GROUP
+
+        if self.group_size == -1 and self.strategy != "channel":
+            raise ValueError("Marlin kernels require group quantization or "
+                             "channelwise quantization, but found no group "
+                             "size and strategy is not channelwise.")
+
+        if num_bits not in WNA16_SUPPORTED_TYPES_MAP:
+            raise ValueError(
+                f"Unsupported num_bits = {num_bits}. "
+                f"Supported num_bits = {WNA16_SUPPORTED_TYPES_MAP.keys()}")
+
+        self.quant_type = WNA16_SUPPORTED_TYPES_MAP[num_bits]
+
+    @classmethod
+    def get_min_capability(cls) -> int:
+        # ampere and up
+        return 80
+
+    def create_weights(self, layer: torch.nn.Module, output_size: int,
+                       input_size: int, output_partition_sizes: List[int],
+                       input_size_per_partition: int,
+                       params_dtype: torch.dtype, weight_loader: Callable,
+                       **kwargs):
+
+        output_size_per_partition = sum(output_partition_sizes)
+
+        mp_linear_kernel_config = MPLinearLayerConfig(
+            full_weight_shape=(input_size, output_size),
+            partition_weight_shape=\
+                (input_size_per_partition, output_size_per_partition),
+            weight_type=self.quant_type,
+            act_type=params_dtype,
+            group_size=self.group_size,
+            zero_points=False,
+            has_g_idx=self.has_g_idx
+        )
+
+        kernel_type = choose_mp_linear_kernel(mp_linear_kernel_config)
+
+        if kernel_type.__name__ not in self._kernel_backends_being_used:
+            logger.info("Using %s for CompressedTensorsWNA16",
+                        kernel_type.__name__)
+            self._kernel_backends_being_used.add(kernel_type.__name__)
+
+        # If group_size is -1, we are in channelwise case.
+        group_size = self.group_size if self.group_size != -1 else input_size
+        row_parallel = (input_size != input_size_per_partition)
+        partition_scales = not marlin_repeat_scales_on_all_ranks(
+            self.has_g_idx, self.group_size, row_parallel)
+
+        scales_and_zp_size = input_size // group_size
+
+        if partition_scales:
+            assert input_size_per_partition % group_size == 0
+            scales_and_zp_size = input_size_per_partition // group_size
+
+        weight = PackedvLLMParameter(input_dim=1,
+                                     output_dim=0,
+                                     weight_loader=weight_loader,
+                                     packed_factor=self.pack_factor,
+                                     packed_dim=1,
+                                     data=torch.empty(
+                                         output_size_per_partition,
+                                         input_size_per_partition //
+                                         self.pack_factor,
+                                         dtype=torch.int32,
+                                     ))
+
+        weight_scale_args = {
+            "weight_loader":
+            weight_loader,
+            "data":
+            torch.empty(
+                output_size_per_partition,
+                scales_and_zp_size,
+                dtype=params_dtype,
+            )
+        }
+        if not partition_scales:
+            weight_scale = ChannelQuantScaleParameter(output_dim=0,
+                                                      **weight_scale_args)
+        else:
+            weight_scale = GroupQuantScaleParameter(output_dim=0,
+                                                    input_dim=1,
+                                                    **weight_scale_args)
+
+        # A 2D array defining the original shape of the weights
+        # before packing
+        weight_shape = BasevLLMParameter(data=torch.empty(2,
+                                                          dtype=torch.int64),
+                                         weight_loader=weight_loader)
+
+        layer.register_parameter("weight_packed", weight)
+        layer.register_parameter("weight_scale", weight_scale)
+        layer.register_parameter("weight_shape", weight_shape)
+
+        # group index (for activation reordering)
+        if self.has_g_idx:
+            weight_g_idx = RowvLLMParameter(data=torch.empty(
+                input_size_per_partition,
+                dtype=torch.int32,
+            ),
+                                            input_dim=0,
+                                            weight_loader=weight_loader)
+            layer.register_parameter("weight_g_idx", weight_g_idx)
+
+        self.kernel = kernel_type(mp_linear_kernel_config,
+                                  w_q_param_name="weight_packed",
+                                  w_s_param_name="weight_scale",
+                                  w_zp_param_name=None,
+                                  w_gidx_param_name="weight_g_idx")
+
+    # Checkpoints are serialized in compressed-tensors format, which is
+    # different from the format the kernel may want. Handle repacking here.
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+        self.kernel.process_weights_after_loading(layer)
+
+    def apply_weights(self, layer: torch.nn.Module, x: torch.Tensor,
+                      bias: Optional[torch.Tensor]) -> torch.Tensor:
+        return self.kernel.apply_weights(layer, x, bias)

vllm/model_executor/layers/quantization/compressed_tensors/utils.py

@@ -0,0 +1,269 @@
+import re
+from enum import Enum
+from typing import Any, Dict, Iterable, Optional, Union
+
+from pydantic import BaseModel, Field, field_validator
+from torch.nn import Module
+
+from vllm.model_executor.layers.quantization.utils.quant_utils import (
+    FUSED_LAYER_NAME_MAPPING)
+
+
+class CompressionFormat(Enum):
+    dense = "dense"
+    sparse_bitmask = "sparse-bitmask"
+    naive_quantized = "naive-quantized"
+    float_quantized = "float-quantized"
+    int_quantized = "int-quantized"
+    pack_quantized = "pack-quantized"
+    marlin_24 = "marlin-24"
+
+
+class QuantizationType(str, Enum):
+    """
+    Enum storing quantization type options
+    """
+
+    INT = "int"
+    FLOAT = "float"
+
+
+class QuantizationStrategy(str, Enum):
+    """
+    Enum storing quantization strategy options
+    """
+
+    TENSOR = "tensor"
+    CHANNEL = "channel"
+    GROUP = "group"
+    BLOCK = "block"
+    TOKEN = "token"
+
+
+class ActivationOrdering(str, Enum):
+    """
+    Enum storing strategies for activation ordering
+
+    Group: reorder groups and weight\n
+    Weight: only reorder weight, not groups. Slightly lower latency and
+    accuracy compared to group actorder\n
+    """
+
+    GROUP = "group"
+    WEIGHT = "weight"
+
+
+class QuantizationArgs(BaseModel):
+    """
+    User facing arguments used to define a quantization config 
+    for weights or activations
+
+    :param num_bits: quantization bit depth
+    :param type: dtype to quantized to, either int or float
+    :param symmetric: whether or not quantization scale is symmetric
+    :param strategy: string determining the scope of scale/zero-point to apply
+    :param group_size: group length to use for the group strategy
+    :param block_structure: 2d block structure to use for the block 
+    strategy, must be of the format "2x4", "8x16", etc.
+    :param dynamic: set True to perform dynamic quantization -
+        values will not be calibrated during calibration phase, 
+        instead during inference new quantization ranges will be 
+        observed with every sample. Defaults to False for static
+        quantization. Note that enabling dynamic quantization 
+        will change the default observer to a memoryless one
+    :param actorder: whether to apply group quantization in decreasing order of
+        activation. Defaults to None for arbitrary ordering
+    """
+
+    num_bits: int = 8
+    type: QuantizationType = QuantizationType.INT
+    symmetric: bool = True
+    group_size: Optional[int] = None
+    strategy: Optional[QuantizationStrategy] = None
+    block_structure: Optional[str] = None
+    dynamic: bool = False
+    actorder: Union[ActivationOrdering, bool, None] = None
+    observer: str = Field(
+        default="minmax",
+        description=("The class to use to compute the quantization param - "
+                     "scale and zero-point'"),
+    )
+    observer_kwargs: Dict[str, Any] = Field(
+        default_factory=dict,
+        description=
+        ("optional dict of kwargs to be passed directly to torch quantization "
+         "Observers constructor excluding quantization range or symmetry"),
+    )
+
+    @field_validator("actorder", mode="before")
+    def validate_actorder(cls, value) -> Optional[ActivationOrdering]:
+        if isinstance(value, bool):
+            return ActivationOrdering.GROUP if value else None
+
+        if isinstance(value, str):
+            return ActivationOrdering(value.lower())
+
+        return value
+
+
+def is_activation_quantization_format(format: str) -> bool:
+    _ACTIVATION_QUANTIZATION_FORMATS = [
+        CompressionFormat.naive_quantized.value,
+        CompressionFormat.int_quantized.value,
+        CompressionFormat.float_quantized.value
+    ]
+    return format in _ACTIVATION_QUANTIZATION_FORMATS
+
+
+def should_ignore_layer(layer_name: Optional[str],
+                        ignore: Iterable[str]) -> bool:
+    if layer_name is None:
+        return False
+
+    # layer_name = model.layers.0.self_attn.qkv_proj
+    # proj_name = qkv_proj
+    proj_name = layer_name.split(".")[-1]
+
+    # Fused layers like gate_up_proj or qkv_proj will not be fused
+    # in the safetensors checkpoint. So, we convert the name
+    # from the fused version to unfused + check to make sure that
+    # each shard of the fused layer has the same scheme.
+    if proj_name in FUSED_LAYER_NAME_MAPPING:
+        shard_proj_names = FUSED_LAYER_NAME_MAPPING[proj_name]
+
+        # Convert fused_name --> [shard_names]
+        shard_names = [
+            layer_name.replace(proj_name, shard_proj_name)
+            for shard_proj_name in shard_proj_names
+        ]
+
+        # Layer should be ignored if shards are ignored.
+        should_ignore_layer = None
+        for shard_name in shard_names:
+            should_ignore_shard = check_equal_or_regex_match(
+                layer_name=shard_name, targets=ignore)
+
+            # If shard_idx=0, set layer ignore to match shard.
+            if should_ignore_layer is None:
+                should_ignore_layer = should_ignore_shard
+
+            # If shard_idx=1+ confirm scheme matches prior shards.
+            elif should_ignore_shard != should_ignore_layer:
+                raise ValueError(f"Found a different quantization schemes for "
+                                 f"{shard_proj_names} in {layer_name}. vLLM "
+                                 "requires all to use the same scheme.")
+
+    # Unfused layers like down_proj and o_proj will match
+    # the safetensors checkpoint already.
+    else:
+        should_ignore_layer = check_equal_or_regex_match(layer_name=layer_name,
+                                                         targets=ignore)
+
+    assert should_ignore_layer is not None
+    return should_ignore_layer
+
+
+def check_equal_or_regex_match(layer_name: str,
+                               targets: Iterable[str]) -> bool:
+    """
+    Checks whether a layer_name is exactly equal or a regex match for 
+    if target starts with 're:' to any target in list.
+    """
+    for target in targets:
+        if _is_equal_or_regex_match(layer_name, target):
+            return True
+    return False
+
+
+def find_matched_target(layer_name: Optional[str], module: Module,
+                        targets: Iterable[str]) -> str:
+    """
+    Helper function to look up which "target" in the compressed-tensors
+    config that a layer corresponds to.
+
+    Recall that a compressed-tensors configs has a concept of 
+    config_groups, where each layer can be quantized with with a different
+    scheme.
+
+    targets in each config_group will be a list of either layer names 
+    (or regexes corresponding to layer names) or names of torch Modules.
+
+    First, we try to match the layer_name with a target
+    Second, we try to match the module's name with a target
+
+    :param layer_name: layer name
+    :param module: torch.nn.Module
+    :param targets: list of targets to match the layer against
+    """
+
+    if layer_name is None:
+        layer_name = ""
+
+    matched_target = (_find_first_match(layer_name, targets)
+                      or _find_first_match(module.__class__.__name__, targets,
+                                           True))
+
+    if matched_target is None:
+        raise ValueError(f"Unable to find matching target for {module} in the "
+                         "compressed-tensors config.")
+
+    return matched_target
+
+
+def _find_first_match(value: str,
+                      targets: Iterable[str],
+                      check_contains: bool = False) -> Optional[str]:
+    """
+    Returns first element of target that matches value either
+    exactly or as a regex after 're:'. If check_contains is set to True,
+    additionally checks if the target string is contained within the value.
+
+    :param value: string to compare the list of targets against
+    :param targets: list of targets to match the layer against
+    :param check_contains: whether or not to do a substring match
+    """
+
+    for target in targets:
+        if _is_equal_or_regex_match(value,
+                                    target,
+                                    check_contains=check_contains):
+            return target
+    return None
+
+
+def get_compressed_tensors_cache_scale(name: str) -> Optional[str]:
+    """
+    Check whether the param name matches the format for k/v cache scales
+    in compressed-tensors. If this is the case, return its equivalent
+    param name expected by vLLM
+
+    :param name: param name
+    :return: matching param name for KV cache scale in vLLM
+    """
+    if name.endswith(".output_scale") and ".k_proj" in name:
+        return name.replace(".k_proj.output_scale", ".attn.k_scale")
+    if name.endswith(".output_scale") and ".v_proj" in name:
+        return name.replace(".v_proj.output_scale", ".attn.v_scale")
+    # If no matches, return None
+    return None
+
+
+def _is_equal_or_regex_match(value: str,
+                             target: str,
+                             check_contains: bool = False) -> bool:
+    """
+    Checks whether a value is exactly equal or a regex match for target
+    if target starts with 're:'. If check_contains is set to True,
+    additionally checks if the target string is contained within the value.
+    """
+
+    if target.startswith("re:"):
+        pattern = target[3:]
+        if re.match(pattern, value):
+            return True
+    elif check_contains:
+        if target.lower() in value.lower():
+            return True
+    elif target == value:
+        return True
+    return False